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贝叶斯优化算法是一种新的演化算法，通过贝叶斯概率统计的知识来学习后代，可是使演化朝有利的方向前进，程序用C实现了贝叶斯优化算法。-Bayesian Optimization Algorithm is a new evolutionary algorithm, through Bayesian probability and statistics to learn the knowledge of future generations, but to enable the evolution to
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用L-M 优化算法与贝叶斯正则化算法训练同一个样本,By LM optimization algorithm with Bayesian regularization algorithm for training with a sample
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采用贝叶斯正则化算法提高bp网络的性能，即L-M优化算法-The use of Bayesian regularization algorithm to improve network performance bp, that is, LM optimization algorithm
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采用贝叶斯正则化算法提高BP网络的推广能力。在本例中，将采用两种训练方法，即L-M优化算法（trainlm)和贝叶斯正则化算法（trainbr),用以训练BP网络，使其能够拟合某一附加有白噪声的正弦样本数据。-The use of Bayesian regularization algorithm for BP network to improve generalization ability. In this case, two types of training methods will b
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动态贝叶斯网络结构学习算法，用来检验基于BOA的DBN结构寻优体系的合理性与可行性。环境matlab 6.1以上-Dynamic Bayesian network structure learning algorithm, the DBN used to test the structure-based optimization BOA system is reasonable and feasible. Environmental matlab 6.1 or above
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采用动量梯度下降算法训练BP网络，采用两种训练方法，即 L-M 优化算法（trainlm）和贝叶斯正则化算法（trainbr），用以训练 BP 网络-Gradient descent algorithm using momentum BP network training, using two training methods, namely, LM optimization algorithm (trainlm) and Bayesian regularization algorithm (t
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Hierarchical Bayesian Optimization Algorithm: Toward a New Generation of Evolutionary Algorithms
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采用两种训练方法，即 L-M 优化算法（trainlm）和贝叶斯正则化算法（trainbr）-Using two training methods, namely, LM optimization algorithm (trainlm) and Bayesian regularization algorithm (trainbr)
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基于大坝温控的温度预报程序，采用了L-M优化算法和贝叶斯正则化算法，结果良好-Prediction based on the temperature of the dam temperature control program, using the LM optimization algorithm and the Bayesian regularization algorithm, good results
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Traditional single particle reconstruction methods use either the Fourier or the delta function basis to represent the particle density map. We propose a more flexible algorithm that adaptively chooses the basis based on the data. Because the basis a
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采用贝叶斯正则化算法提高 BP 网络的推广能力。在本例中，我们采用两种训练方法，即 L-M 优化算法（trainlm）和贝叶斯正 -Bayesian regularization algorithm to improve the generalization ability of BP network. In this example, we use two training methods, namely LM optimization algorithm (trainlm) and Baye
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“Fast Tracking via Dense Spatio-Temporal Context Learning,” In ECCV 2014的源代码，效果非常好。-In this paper, we present a simple yet fast and robust algorithm which exploits the spatio-temporal context for visual tracking. Our approach formulates the spatio-te
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可自由选择贝叶斯正则化算法或者是L-M 优化算法的神经网络matlab代码-The freedom to choose the Bayesian regularization algorithm or LM optimization algorithm neural network matlab code
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部分实现了追踪测速迭代松弛算法，UfKDoAK参数包含优化类的几个简单示例程序，通过matlab代码，是机器学习的例程，YGzNBvR条件验证可用，包括主成分分析、因子分析、贝叶斯分析。- Partially achieved tracking speed iterative relaxation algorithm, UfKDoAK parameter Optimization class contains several simple sample programs, By matlab
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包含优化类的几个简单示例程序，三相光伏逆变并网的仿真，利用贝叶斯原理估计混合logit模型的参数，模式识别中的bayes判别分析算法，计算加权加速度。-Optimization class contains several simple sample programs, Three-phase photovoltaic inverter and network simulation, Bayesian parameter estimation principle mixed logit mode
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包含优化类的几个简单示例程序，借鉴了主成分分析算法（PCA），利用贝叶斯原理估计混合logit模型的参数，插值与拟合，解方程，数据分析，实现典型相关分析，关于小波的matlab复合分析。- Optimization class contains several simple sample programs, It draws on principal component analysis algorithm (PCA), Bayesian parameter estimation princip
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最小二乘回归分析算法，实现串口的数据采集，利用贝叶斯原理估计混合logit模型的参数，LCMV优化设计阵列处理信号，课程设计时编写的matlab程序代码，抑制载波型差分相位调制。-Least-squares regression analysis algorithm, Achieve serial data acquisition, Bayesian parameter estimation principle mixed logit model, LCMV optimization desig
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多抽样率信号处理，用MATLAB实现动态聚类或迭代自组织数据分析，LCMV优化设计阵列处理信号，利用贝叶斯原理估计混合logit模型的参数，最大信噪比的独立分量分析算法，解耦，恢复原信号，仿真效率很高的，gmcalab 快速广义的形态分量分析。- Multirate signal processing, Using MATLAB dynamic clustering or iterative self-organizing data analysis, LCMV optimization des
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例1 采用动量梯度下降算法训练 BP 网络。
例2 采用贝叶斯正则化算法提高 BP 网络的推广能力。在本例中，我们采用两种训练方法，即 L-M 优化算法（trainlm）和贝叶斯正则化算法（trainbr），用以训练 BP 网络，使其能够拟合某一附加有白噪声的正弦样本数据。-Example 1 uses the momentum gradient descent algorithm to train the BP network.
Example 2 uses the Bayesian
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New method of convex optimization in CS
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